Themes, Saving and Interactive Plots

 

These exercise cover the scales, statistics and themes of ggplot2 for Plotting in R.

Exercise 1 - Themes

• Read in the cleaned patients dataset as we saw in ggplot2 course earlier (“patients_clean_ggplot2.txt”)

• Set the global theme to use theme_bw(). Using the patient data set generate a scatter plot of BMI versus Weight. Add a color scale to the scatter plot based on the Pet variable. Use an additional geom to add an extra layer of a fit line to our scatterplot (use lm method). Lets also add a nice color palette of your choice from Colorbrewer, Paleteer or Viridis.

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Remove the legend title from the plot, change the background colors of legend panels to gray and place legend at bottom of plot.

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Remove the legend title from the plot, change the background colors of legend panels to white and place legend at bottom of plot.

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Add a title to the plot, remove minor grid lines and save to 7 by 7 inch png file.

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Save our current theme then try out some alternative themes. Try one theme included with ggplot2 and one from ggthemes.

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Take at least one element from each theme you just tried out and add it to our existing theme.

• Use the + to update our plot to use the new theme

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Save the final plot as a pdf.

## Saving 7 x 5 in image
## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

• Reset the theme to the default: theme_gray()

## `geom_smooth()` using formula = 'y ~ x'

## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).

Exercise 2 - External Packages

• Load in the data from “data/DESeq2_PCA.RData”. This contains a ggplot object that is produced by the package DESeq2. It is a PCA plot produced from differential gene expression analysis. Review the object. Check the plot, data, aesthetics etc.

## Warning: namespace 'DESeq2' is not available and has been replaced
## by .GlobalEnv when processing object 'deseq_pca_example'

##                PC1         PC2 group condition     name
## sample1  -13.24423  22.0512418     A      Ctrl  sample1
## sample2  -14.40362 -13.8514994     A      Ctrl  sample2
## sample3  -14.79663  -2.9299797     A      Ctrl  sample3
## sample4  -15.12221  -9.4008058     A       Mut  sample4
## sample5  -14.78719  -0.2914010     A       Mut  sample5
## sample6  -13.77777   5.6896212     A       Mut  sample6
## sample7   13.68499  -2.4219808     B      Ctrl  sample7
## sample8   14.85294   3.9328670     B      Ctrl  sample8
## sample9   12.21286  -3.0234083     B      Ctrl  sample9
## sample10  16.25675  -5.6924350     B       Mut sample10
## sample11  14.85222   0.9620356     B       Mut sample11
## sample12  14.27188   4.9757444     B       Mut sample12

## Aesthetic mapping: 
## * `colour` -> `group`
## * `x`      -> `PC1`
## * `y`      -> `PC2`

• Lets change the color scheme and add a title.

• When we look at the data column we can see there is an additional column contained in the metadata - condition. Lets add this to the plot.

Exercise 3 - Interactive Plots

• Turn our PCA plot into an interactive plot.

• Add the names of each sample from the orginal data object to the interactive labeling.

## Warning in geom_point(aes(label = name)): Ignoring unknown aesthetics: label

Exercise 4 - Working Example

In this final exercise we will run through a common example: making a volcano plot.

A volcano plot consists of: 1) log2FC on the x axis 2) -log10(pval) on the y axis

It is also good to add some additional customization: - Highlight significant genes i.e. pval <0.05 - Highlight genes above a certain threshold log2FC i.e. >1 - Add lines to denote these thresholds - Label some genes of interest directly: “Gm8714”,“Pas1b”,“Rab39”,“Tmc2”,“Ttpal”,“Ctdsp1” - Use a simple theme to also give the plot a simple look - Export the plot as a pdf

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_text()`).

## Saving 7 x 5 in image

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Removed 2 rows containing missing values or values outside the scale range
## (`geom_text()`).

• To improve labeling on ggplot there is an additional package that can be useful: ggrepel. Try installing ggrepel and using geom_text_repel.

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).

## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).

• Make it dynamic and interactive with plotly just labeling the SYMBOL information.

myplot <- ggplot(my_res, aes(x=log2FoldChange, 
                   y=-log10(pvalue), 
                   color = ifelse(pvalue > 0.05, "NS",
                                 ifelse(log2FoldChange > 1, "SigUp", 
                                        ifelse(log2FoldChange < (-1), "SigDown", "Sig"))))) +
  geom_point(size=0.5, alpha=0.5) +
  scale_color_manual(name = "Significance", breaks=c("NS", "Sig", "SigUp", "SigDown"), values = c("black", "green","blue", "red")) +
  theme_bw() + 
  ggtitle("Volcano Plot showing significance of \ngene expression changes following DESeq analysis") + 
  geom_hline(yintercept=(-log10(0.05)), lty =3, color="gray") + 
  geom_vline(xintercept=c((-1),1), lty =3, color="gray") + 
  theme(text = element_text(size = 8))

ggplotly(myplot + geom_point(aes(text = SYMBOL )), source = "select", tooltip = c("SYMBOL"))

## Warning in geom_point(aes(text = SYMBOL)): Ignoring unknown aesthetics: text